With the advent of artificial intelligence in recent years, chatbots have attracted lots of attent ion from both academia and industry. Many commercial products, such as Apple's Siri, Amaz on's Echo, Google's Assistant, Microsoft's XiaoIce and Cortana, etc., have been beginning to a ppear in people's lives while changing their interactive ways.

Different to the chitchat bots, chatbots in customer service are almost task-oriented or solvi ng information-seek problems. They are always expected to deliver fast and reliable respons es across channels wherever the customers could use. Typically, they answer questions base d on faq dataset and text matching algorithms. The matching algorithm accepts two question s, in which one is the utterance from the user and the other is the question from the FAQ dataset, then decides whether they are semantically similarity. At Alibaba, we have built up t he biggest chatbot platform which is called AliMe bot platform in China and many scenarios could be modeled within this similar framework. However, the shortcomings of this framework are always obvious. This framework is essentially not a fra mework of dialogue system since it may not recognize the way people naturally speak.

It is also not a comprehensive view to think about chatbot systems in customer service. Fro m another perspective of working mode of them, they could be divided into two categories, i n which one is automatic mode while the other is semi-automatic mode. Obviously, the abov e framework is working in automatic mode. However, chatbots working in semi-automatic mode are also needed in real scenarios. In this mode, human workers could intervene the int eractive flow between the system and the customer. The chatbots would collaborate with hu man workers and suggest actions for them to reduce their workload.

We invite researchers who are either experts or are keenly aware of the challenges and oppo rtunities that their fields bring to these two topics to work on new dialog managers in custo mer service, allowing us to stay focused on real-world applications.

Target

We notice that the current research works of task-oriented dialogue systems are relatively s cattered, e.g. some researchers are focused on the coherence of response generation, while t he others are focused on context understanding, etc. Due to the different focuses of research ers, the whole dialog manager for fully automatic mode or semi-automatic mode has been fa r from exploration.

In this proposal, we make some efforts to explore the core characteristics of chatbots in cust omer service. One is personalized interactive strategy in automatic mode and the other one i s human-machine collaborative mechanism in semi-automatic mode. These two characterist ics are related to the core module, dialog manager in the dialogue system. We hope that these efforts could help both academia and industry find new dialog managers.

Related Research Topics

Recently, the methods based on representing the users and other personalized features in ve ctor space have become widely popular. The embeddings are inputted to a neural network a nd the parameters are learning from the training data. Obtaining such a dense vector is inher ently difficult and poses several challenges where related research topics may arise:

Sparsity: The raw data, such as dialog logs, are dirty. Meanwhile, due to the lack of diversit y of raw data, personalized data is sparse.

Controllable: Typical technology of text generation is based on a sequence to sequence fra mework. However, these typical methods are lack of controllability, which is a key ability for online service system.

Multimodal: According to the personalized characteristic, the new dialog manager is hope d to decide the interactive actions, such as text description or video shows, etc.

In the semi-automatic working mode, we aim to help human workers to reduce workload an d continuously improve to maximize user satisfaction. Related research topics may arise as follows:

Collaborative Mechanism: In the semi-automatic chatbots, various system actions, such as answer recommendations or keeping silence, would be defined. Besides this, the strategy ne twork would be learnt from the data by reinforcement learning algorithms. Briefly, the dialo g manager would have the abilities of continuously improving the user satisfaction and human’s workload during collaboration.